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Yayın Computational model of the ventricular action potential in adult spontaneously hypertensive rats(2003-09-01) Padmala, Srikanth; Demir, Sıddıka SemahatIntroduction: Cardiac hypertrophy has substantial clinical significance because many hypertrophic cells have markedly prolonged repolarization behavior, which may lead to increased risk for cardiac arrhythmias. Spontaneously hypertensive rat (SHR) is one model of hypertension that is studied extensively and is considered to be the best laboratory model of human hypertension. We extended our previously published model of the rat ventricular myocyte to simulate the effects of hypertrophy in SHR. Methods and Results: In SHR it has been shown that the membrane capacitance is increased, the density of transient outward K+ current is decreased, the sarcoplasmic reticulum Ca 2+ ATPase activity is reduced, and the cell volumes are increased compared to those of the normal rat. We introduced these changes into our previous model of the rat ventricular myocyte and simulated the ventricular action potential of SHR. Our results demonstrated increased action potential duration (APD) and increased peak systolic value of the intracellular calcium transient in SHR. Simulations with reduced extracellular K+ concentration ([K+]o) have shown that there is increased APD shortening in SHR compared to that of the normal rat. Conclusions: Our computational model qualitatively simulated the electrophysiologic changes observed in SHR and provided the plausible mechanistic linkage between the prolonged APD and increased inotropy. Our model results also demonstrated the electrophysiologic changes observed with reduced [K+]o in SHR, a finding that is clinically significant in hypertensive patients with left ventricular hypertrophy undergoing diuretic treatment.Yayın Developing an efficient deep neural network for automatic detection of COVID-19 using chest X-ray images(Elsevier B.V., 2021-06) Sheykhivand, Sobhan; Mousavi, Zohreh; Mojtahedi, Sina; Yousefi Rezaii, Tohid; Farzamnia, Ali; Meshgini, Saeed; Saad, IsmailThe novel coronavirus (COVID-19) could be described as the greatest human challenge of the 21st century. The development and transmission of the disease have increased mortality in all countries. Therefore, a rapid diagnosis of COVID-19 is necessary to treat and control the disease. In this paper, a new method for the automatic identification of pneumonia (including COVID-19) is presented using a proposed deep neural network. In the proposed method, the chest X-ray images are used to separate 2–4 classes in 7 different and functional scenarios according to healthy, viral, bacterial, and COVID-19 classes. In the proposed architecture, Generative Adversarial Networks (GANs) are used together with a fusion of the deep transfer learning and LSTM networks, without involving feature extraction/selection for classification of pneumonia. We have achieved more than 90% accuracy for all scenarios except one and also achieved 99% accuracy for separating COVID-19 from healthy group. We also compared our deep proposed network with other deep transfer learning networks (including Inception-ResNet V2, Inception V4, VGG16 and MobileNet) that have been recently widely used in pneumonia detection studies. The results based on the proposed network were very promising in terms of accuracy, precision, sensitivity, and specificity compared to the other deep transfer learning approaches. Depending on the high performance of the proposed method, it can be used during the treatment of patients.
